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Vaccines
Special Issues
Computer and Technology Supported Development of Vaccines 2.0
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Computer and Technology Supported Development of Vaccines 2.0

A special issue of Vaccines (ISSN 2076-393X). This special issue belongs to the section "Vaccination Optimization".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2364

Special Issue Editors

Dr. Pedro A. Reche

E-Mail Website
Guest Editor
Laboratory of Immunomedicine, Department of Immunology & O2, School of Medicine, Complutense University of Madrid, Pza Ramon y Cajal, S/N, 28040 Madrid, Spain
Interests: immunoinformatics; protein engineering; antigen presentation and processing; HLA; epitope; prediction; immunotherapy; vaccines
Prof. Dr. Francesco Pappalardo

E-Mail Website
Guest Editor
Department of Drug Sciences, University of Catania, 95125 Catania, Italy
Interests: computational immunology; agent-based models; in silico trials; regulatory affairs; systems biomedicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Early vaccines were obtained with little knowledge or technology, and required much time as well as effort for their development. In contrast, ongoing COVID-19 pandemics have underscored our current ability to develop vaccines quickly. In this context, the main aim of this Special Issue is to put forward new knowledge, concepts, and technologies that can be recruited to facilitate vaccine design, including contributions from proteomics, immunology, structural biology, systems biology, and mathematical modeling. In this context, the adoption of modeling and simulation for the development and de-risking of vaccines will be the key to drastically reducing animal and human testing, lowering costs, and shortening marketing times.

We particularly welcome research articles and reviews on the complete-genome sequencing of pathogens, computational and high-throughput technologies for antigen and epitope discovery, antigen receptor profiling, the modeling of antigen–antigen receptor interactions, and the computational simulation of immune responses. Likewise, contributions utilizing these technologies to develop vaccines and immunotherapeutics to treat cancer, allergies, and autoimmune diseases are also welcome.

Dr. Pedro A. Reche
Prof. Dr. Francesco Pappalardo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vaccines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • antigen
  • epitope
  • immunoinformatics
  • vaccines
  • computational immune simulations

Published Papers (1 paper)

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Research

15 pages, 3907 KiB  
Article
ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19
by Sovan Saha, Piyali Chatterjee, Anup Kumar Halder, Mita Nasipuri, Subhadip Basu and Dariusz Plewczynski
Vaccines 2022, 10(10), 1643; https://doi.org/10.3390/vaccines10101643 - 30 Sep 2022
Cited by 6 | Viewed by 1992
Abstract
Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same [...] Read more.
Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study. Full article
(This article belongs to the Special Issue Computer and Technology Supported Development of Vaccines 2.0)
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